Systematic review of bankruptcy prediction models: Towards a framework for tool selection
The bankruptcy prediction research domain continues to evolve with many new different
predictive models developed using various tools. Yet many of the tools are used with the …
predictive models developed using various tools. Yet many of the tools are used with the …
Artificial neural networks in business: Two decades of research
M Tkáč, R Verner - Applied Soft Computing, 2016 - Elsevier
In recent two decades, artificial neural networks have been extensively used in many
business applications. Despite the growing number of research papers, only few studies …
business applications. Despite the growing number of research papers, only few studies …
Towards augmented kernel extreme learning models for bankruptcy prediction: algorithmic behavior and comprehensive analysis
Y Zhang, R Liu, AA Heidari, X Wang, Y Chen, M Wang… - Neurocomputing, 2021 - Elsevier
Bankruptcy prediction is a crucial application in financial fields to aid in accurate decision
making for business enterprises. Many models may stagnate to low-accuracy results due to …
making for business enterprises. Many models may stagnate to low-accuracy results due to …
Real‑time COVID-19 diagnosis from X-Ray images using deep CNN and extreme learning machines stabilized by chimp optimization algorithm
T Hu, M Khishe, M Mohammadi, GR Parvizi… - … Signal Processing and …, 2021 - Elsevier
Real-time detection of COVID-19 using radiological images has gained priority due to the
increasing demand for fast diagnosis of COVID-19 cases. This paper introduces a novel two …
increasing demand for fast diagnosis of COVID-19 cases. This paper introduces a novel two …
Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods
Z Yang, L Ce, L Lian - Applied Energy, 2017 - Elsevier
Electricity prices have rather complex features such as high volatility, high frequency,
nonlinearity, mean reversion and non-stationarity that make forecasting very difficult …
nonlinearity, mean reversion and non-stationarity that make forecasting very difficult …
Artificial intelligence in business and economics research: Trends and future
Artificial Intelligence is a disruptive technology developed during the 20th century, which has
undergone an accelerated evolution, underpinning solutions to complex problems in the …
undergone an accelerated evolution, underpinning solutions to complex problems in the …
Estimation of moment and rotation of steel rack connections using extreme learning machine
The estimation of moment and rotation in steel rack connections could be significantly
helpful parameters for designers and constructors in the initial designing and construction …
helpful parameters for designers and constructors in the initial designing and construction …
Bankruptcy visualization and prediction using neural networks: A study of US commercial banks
FJL Iturriaga, IP Sanz - Expert Systems with applications, 2015 - Elsevier
We develop a model of neural networks to study the bankruptcy of US banks, taking into
account the specific features of the recent financial crisis. We combine multilayer …
account the specific features of the recent financial crisis. We combine multilayer …
Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models
Accurate prediction of groundwater level (GWL) fluctuations can play an important role in
water resources management. The aims of the research are to evaluate the performance of …
water resources management. The aims of the research are to evaluate the performance of …
Grey wolf optimization evolving kernel extreme learning machine: Application to bankruptcy prediction
This study proposes a new kernel extreme learning machine (KELM) parameter tuning
strategy using a novel swarm intelligence algorithm called grey wolf optimization (GWO) …
strategy using a novel swarm intelligence algorithm called grey wolf optimization (GWO) …